Histopathological image classification using deep learning

This thesis presents a study of digital pathology classification using and combining several techniques of machine learning and deep learning. Cancer is one of the most common causes of death around the world. One of the main complications of the disease is the prediction in the final stage. Nowadays there are many different studies to obtain a correct diagnosis on time. Some of these studies are tissue biopsies. These samples are analyzed by a pathologist, which must observe pixel by pixel a whole image of high dimensions to give a diagnostic of the disease, including stage and class. This activity takes weeks, even for experts, because usually several samples are extracted from a single patient. To speed up and facilitate this process, several models have been developed for digital pathology classification. With these models, it is easier to discard many patient slides than the traditional method, then, the main activity for a pathologist is to confirm a diagnosis with the most relevant or complicated sample. The downside of these models is that most of them are based on deep learning, a technique that is well known for its great performance, but also for its high requirements like graphic processors and memory resources. Consequently, we performed a complete analysis of several convolutional neural networks used in different ways to compare outcomes and efficiency. In addition, we include techniques such as recurrent neural networks and machine learning. Several models of deep learning and machine learning are presented as alternatives to convolutional neural networks, including 5 computer vision techniques. The main objective of our project is to perform a real alternative capable to achieve similar outcomes to deep learning with limited resources. The experiments were successful, including a real alternative for deep learning for the classification of 3 different types of cancer with an area under the curve higher than 90%.